Deep Learning in Healthcare Summit schedule

How Deep Learning is Transforming Drug Discovery

By driving cars, beating humans at their own games, transcribing speech and translating text, deep learning is changing the world. However, over the past five years, exponential growth in biomedical datasets has created the perfect opportunity for deep learning to disrupt drug discovery. Already, deep learning systems can identify disease mutations more reliably than human experts, and can predict which compounds will modulate the activity of therapeutic targets. I'll describe different approaches to drug discovery and explain how Deep Genomics is using its machine learning platform to identify therapies for metabolic, neuromuscular and neurodegenerative disorders, and to advance them into clinical trials as fast as possible. I'll also describe our approach to overcoming the so-called 'black box' challenge of establishing trust with stakeholders, regulators, clinicians and patients.

Brendan Frey is internationally recognized as a leader in machine learning and genome biology. His group has published over a dozen papers in Science, Nature and Cell, and their most recent work on using deep learning to identify protein-DNA interactions was highlighted on the front cover Nature Biotechnology. Frey is a Fellow of the Royal Society of Canada, a Fellow of the Institute for Electrical and Electronic Engineers, and a Fellow of the American Association for the Advancement of Science. He has consulted for several industrial research and development laboratories in Canada, the United States and England, and has served on the Technical Advisory Board of Microsoft Research. Most recently, Dr. Frey spun out a company called Deep Genomics.

Alejandro (Alex) Jaimes is CTO & Chief Scientist at Acesio. Acesio focuses on Big Data for predictive analytics in Healthcare to tackle disease at worldwide scale, impacting individuals and entire populations. We use Artificial Intelligence to collect and analyze vast quantities of data to track and predict disease in ways that have never been done before- leveraging environmental variables, population movements, sensor data, and the web. Prior to joining Acesio, Alex was CTO at AiCure and prior to that he was Director of Research/Video Product at Yahoo where he led research and contributions to Yahoo's video products, managing teams of scientists and engineers in New York City, Sunnyvale, Bangalore, and Barcelona. His work focuses on Machine Learning, mixing qualitative and quantitative methods to gain insights on user behavior for product innovation. He has published widely in the top-tier conferences (KDD, WWW, RecSys, CVPR, ACM Multimedia, etc), has been a visiting professor (KAIST), and is a frequent speaker at international academic and industry events. He is a scientist and innovator with 15+ years of international experience in research leading to product impact (Yahoo, KAIST, Telefonica, IDIAP-EPFL, Fuji Xerox, IBM, Siemens, and AT&T Bell Labs). He has worked in the USA, Japan, Chile, Switzerland, Spain, and South Korea, and holds a Ph.D. from Columbia University.

Mobile Cognitive Healthcare, Deep Learning, and Artificial Intelligence

Every two years, the world’s database information doubles, and every three years medical information also doubles. By 2020, global healthcare data will double every three days, with 80% of the data being unstructured, or not in tabular form. To truly personalize medicine, this healthcare data needs to translate into accurate and actionable recommendations for both patients and physicians.

OME™ is a mobile consumer general health and wellness application that uses a Pathway A.I. and Cognitive Computing (IBM Watson), linked with genetic testing and other personal user information. OME™ collects and manages any type of personal health data (genetic tests, lab data, or wearable information) and dynamically delivers important personalized information to the user or physician.

Dr. Nova is currently the Chief Innovation Officer of Pathway Genomics, and was a founding team member of the company. He is the inventor of the Pathway-IBM/Watson Deep Machine Learning and Artificial Intelligence mobile application: Panorama/OME. His scientific career began as a research associate at the Salk Institute in the laboratory of Nobel Laureate Roger Guillemin. Michael was previously the founder and CEO of Discovery Partners Inc. (Nasdaq: DPII), which completed a successful $150M IPO. Dr. Nova is also the 2005 World Economic Forum (WEF) Technology Pioneer Award Winner; and the physician of record on the first person to ever have their entire genome sequenced by Illumina (2009). He is a member of the IBM Watson, Metagenics, and Salk Institute NeuroAI Advisory Boards.

Deep Learning in Medical Imaging - Successes and Challenges

Machines capable of analysing and interpreting medical scans with super-human performance are within reach. Deep learning, in particular, has emerged as a promising tool in our work on automatically detecting brain damage. But getting from the lab into clinical practice comes with great challenges. How do we know when the machine gets it wrong? Can we predict failure, and can we make the machine robust to changes in the clinical data? We will discuss some of our most recent work that aims to address these critical issues and demonstrate our latest results on deep learning for analysing medical scans.

Ben Glocker is Senior Lecturer in Medical Image Computing at the Department of Computing at Imperial College London, and one of three academics leading the Biomedical Image Analysis Group. He also leads the HeartFlow-Imperial Research Team and is scientific advisor for London-based start-up Kheiron Medical Technologies. His research is at the intersection of medical image analysis and artificial intelligence aiming to build computational tools for improving diagnosis, therapy and intervention. He has received several awards including a Philips Impact Award and the Francois Erbsmann Prize. He is a member of the Young Scientists Community of the World Economic Forum. His ERC Starting Grant MIRA is devoted to developing the next generation machine intelligence for medical image representation and analysis.

Turning Everyday Devices into Health Sensors

Today's electronics have very sensitive optical and motion sensors. These can captures subtle signals resulting from cardiorespiratory activity. I will present how webcam(s) can be used to measure important physiological parameters without contact with the body. In addition, I will show how an ordinary smartphones can be turned into a continuous physiological monitors. Both of these techniques reveal the surprising power of devices with around us all the time. I will show how deep learning are helping us create highly scalable and low-cost applications based on these sensor measurements.

Daniel McDuff is Principal Research Scientist at Affectiva. He is building and utilizing scalable computer vision and machine learning tools to enable the automated recognition and analysis of emotions and physiology. At Affectiva Daniel is building state-of-the-art facial expression recognition software and leading analysis of the world's largest database of human emotions (currently with 8B+ data points). Daniel completed his PhD in the Affective Computing Group at the MIT Media Lab in 2014 and has a B.A. and Masters from Cambridge University. His work has received nominations and awards from Popular Science magazine as one of the top inventions in 2011, South-by-South-West Interactive (SXSWi), The Webby Awards, ESOMAR and the Center for Integrated Medicine and Innovative Technology (CIMIT). His work has been reported in many publications including The Times, the New York Times, The Wall Street Journal, BBC News, New Scientist and Forbes magazine. Daniel is also a Research Affiliate at the MIT Media Lab.

Deep Learning: Modular in Theory, Inflexible in Practice

The high-level view of deep learning is elegant: composing differentiable components together trained in an end-to-end fashion. Deep learning seems like a perfect tool for enabling novel medical imaging applications by tackling some of its unique challenges such as large and high-dimensional datasets with unorthodox structure, extremely fine signals, and massive diversity with limited data. The reality isn't that simple, and the commonly used tools greatly limit what we are capable of doing. In this talk, we will discuss some of the unique challenges to medical deep learning, what we can do about it, and how those things can result in much better models in practice.

Diogo Moitinho de Almeida is a data scientist, software engineer, and hacker. He has previously been a medalist at the International Math Olympiad ending a 13-year losing streak for the Philippines, received the top prize in the Interdisciplinary Contest in Modeling achieving the highest distinction of any team from the Western Hemisphere, and won a Kaggle competition setting a new state-of-the-art for black box identification of causality and getting the opportunity to speak at the Conference on Neural Information Processing Systems. As a lifelong learner and big fan of online education, he has taken more classes online than he has getting his undergraduate degree at the Rensselaer Polytechnic Institute. He loves all things software and enjoys contributing to open source, giving talks on things that he's built, and improving his Emacs setup.

Challenges for Delivering Machine Learning in Health

The wealth of data availability presents new opportunities in health but also challenges. In this talk we will focus on challenges for machine learning in health: 1. Paradoxes of the Data Society, 2. Quantifying the Value of Data, 3. Privacy, loss of control, marginalization. Each of these challenges has particular implications for machine learning. The paradoxes relate to our evolving relationship with data and our changing expectations. Quantifying value is vital for accounting for the influence of data in our new digital economies and issues of privacy and loss of control are fundamental to how our pre-existing rights evolve as the digital world encroaches more closely on the physical. One of the goals of research community should be to provide the technological tooling to address these challenges ensure that we are empowered to avoid the pitfalls of the data driven society, allowing us to reap the benefits of machine learning in applications from personalized health to health in the developing world.

Neil Lawrence is a Professor of Machine Learning at the University of Sheffield, currently on leave of absence at Amazon, Cambridge. His main technical research interest is machine learning through probabilistic models. He focuses on both the algorithmic side of these models and their application. He has a particular interest on applications in personalized health and applications in the developing world. Neil is well known for his work with Gaussian processes, and has proposed Gaussian process variants of many of the successful deep learning architectures. He is also an advocate of of the ideas behind “Open Data Science” and active in public awareness (see https://www.theguardian.com/profile/neil-lawrence) and community organization. He has been both program chair and general chair of the NIPS Conference.

Changing the Drug Discovery Paradigm

The process of drug discovery and development has fundamentally remained unaltered for the last 30 years, despite the use of modern tools and technologies. A radically new approach is needed to effectively explore the opportunities provided by the wealth of biomedical data to enable the development of new and effective treatments. Stratified Medical integrates leading edge predictive analytics, visualisation tools and machine learning to enable experienced drug hunters to explore huge, highly interconnected biomedical knowledge spaces in innovative and unprecedented ways. Stratified Medical is also committed to helping the biomedical community to better access and utilise scientific data by making tools available to the community for example ferret.

Jackie Hunter is the Chief Executive Officer of Stratified Medical. Stratified Medical unites traditional pharmaceutical development methodology with Artificial Intelligence to augment the research capabilities of its drug scientists so that they can gain new insights to increase the efficiency of medicines development. Jackie has over thirty years of experience in the bioscience research sector, working across academia and industry including leading neurology and gastrointestinal drug discovery and early childhood development for GlaxoSmithKline. She founded OI Pharma Partners in 2010 to support the life science sector in harnessing the power of open innovation and most recently was chief executive of the Biotechnology and Biological Sciences Research Council. She holds a personal chair from St George's Hospital Medical School, which was awarded in recognition of her contribution to bioscience research. In 2010 she was awarded a CBE in the Queen's Birthday Honours list for Services to the Pharmaceutical Industry.

Cosima is a doctor, health tech consultant and entrepreneur. She has a background in psychology and a special interest in the challenges of designing technology for use in expert domains. She is currently working with Outcomes Based Healthcare on a project funded by Innovate UK using machine learning and smartphone sensor data to passively monitor health outcomes in diabetics. She is an alumnus of the Singularity University Graduate Studies Programme and currently an Academic Foundation trainee in Psychiatry and Psychological Medicine at St Thomas’s Hospital. She is the founder of AXNS Collective, a science communication company funded by the Wellcome Trust and the Arts Council curative collaborations between artists and scientists.

How Artificial Intelligence will Change the Face of Healthcare

Everyone in the world is facing differing degrees of the same issue - the accessibility and affordability of healthcare. For some, the problem is convenience, cost or speed. For others, the issue is more serious with almost 50% of the world having little access to quality healthcare. Yet, four unstoppable trends are coming together to see the creative reconstruction of medicine within the next decade. The result will be a service that is more accessible, effective and democratic, irrespective of where people live. Today, everyone has near equal access to everything that is digital. The same may soon be happening to healthcare, and these trends show why: 1 - Diagnostics is improving at double the rate of Moore’s Law 2 - Information is free and getting smarter 3 - Smartphones and “the internet of everything” will create a global channel of healthcare delivery 4 - Intervention will be unrecognisable

These four trends are melting all that is solid in medicine into air. How it will develop no one can know, but one thing is for sure; a very different model of healthcare delivery is unfolding, and it should make the future of healthcare significantly smarter and better value for everyone.

Ali is an engineer and healthcare entrepreneur, and the founder and CEO of babylon, the UK’s leading digital healthcare service. Its purpose is to democratise healthcare by putting an accessible and affordable health service into the hands of every person on earth. In order to achieve this the company is bringing together one of the largest teams of scientists, clinicians, mathematicians and engineers to focus on combining the ever-growing computing power of machines with the best medical expertise of humans to create a comprehensive, immediate and personalised health service and make it universally available. Launched in February 2015, the service now has over 600,000 registered users globally.. babylon’s home grown success has seen the company expand into Europe and Africa in 2016, with a second head office located in Rwanda. Around 120 businesses, including Citigroup, BNY Mellon, LinkedIn and leading employee benefits and health insurance providers have partnered with babylon to offer its services to UK employees. Further, the company has partnered with the NHS to make its services available to the broader UK population. Prior to Babylon, Ali founded Circle and built it within a few years to become Europe's largest partnership of clinicians, with some £200m of revenue, near 3,000 employees and a successful IPO. Earlier, Ali was the recipient of the Royal Award for the Young Entrepreneur of the year for founding his first business, V&G, and the Healthcare Entrepreneurial Achievement Award for establishing Circle. Ali was named by The Times among the 100 global people to watch, and by HSJ among the 50 most influential people in healthcare. Ali is the UK Cabinet Office Ambassador for Mutuals and has a PhD in Engineering Physics.

Alejandro (Alex) Jaimes is CTO & Chief Scientist at Acesio. Acesio focuses on Big Data for predictive analytics in Healthcare to tackle disease at worldwide scale, impacting individuals and entire populations. We use Artificial Intelligence to collect and analyze vast quantities of data to track and predict disease in ways that have never been done before- leveraging environmental variables, population movements, sensor data, and the web. Prior to joining Acesio, Alex was CTO at AiCure and prior to that he was Director of Research/Video Product at Yahoo where he led research and contributions to Yahoo's video products, managing teams of scientists and engineers in New York City, Sunnyvale, Bangalore, and Barcelona. His work focuses on Machine Learning, mixing qualitative and quantitative methods to gain insights on user behavior for product innovation. He has published widely in the top-tier conferences (KDD, WWW, RecSys, CVPR, ACM Multimedia, etc), has been a visiting professor (KAIST), and is a frequent speaker at international academic and industry events. He is a scientist and innovator with 15+ years of international experience in research leading to product impact (Yahoo, KAIST, Telefonica, IDIAP-EPFL, Fuji Xerox, IBM, Siemens, and AT&T Bell Labs). He has worked in the USA, Japan, Chile, Switzerland, Spain, and South Korea, and holds a Ph.D. from Columbia University.

Reza is currently the Chief Scientist at AIG (leading the company's AI research and InsurTech innovations, globally), as well as one of the co-leads of Deep Medicine program at the University of Oxford's Martin School (focused on healthcare innovation, and the use of AI in digital health ecosystems). He obtained his DPhil (i.e., PhD) in computational neuroscience and machine learning from the University of Oxford in 2010, and since then has been leading teams, researches and disruptive innovation projects in both academia and industry.

Mahiben is a practicing doctor and Senior Fellow to the CEO of NHS England, advising on £100 billion of annual health spending, with a focus on innovation and technology. He co-founded the NHS Innovation Accelerator and the NHS Diabetes Prevention Programme. Mahiben has advised a range of organisations, from start-ups to multilaterals, including the Swiss government and the WHO. He has a strong interest in research with over 80 peer-reviewed publications and 50 academic awards. Mahiben was educated at Oxford, Cambridge and Harvard universities; he was the first person from British healthcare to be included in Forbes’ 30 under 30.

I joined Playfair Capital in 2013 to focus on deal sourcing, due diligence, and helping our companies grow. I'm particularly interested in artificial intelligence and machine learning, infrastructure-as-a-service, mobile, and bioinformatics. I've led, originated or participated in Seed through Growth investments including Mapillary, Appear Here, Dojo, and Festicket.

Prior to Playfair, I earned an M.Phil and Ph.D in oncology as a Gates and Dr. Herchel Smith Scholar at the University of Cambridge, and a BA in biology from Williams College. I've published research focused on technologies to halt the fatal spread of cancer around the body.

At BioBeats, we're working on projects that help people be well, fight stress, and be more productive. In most of these projects, deep learning approaches are taken to train models that can classify, predict and illuminate behaviour from the person's body and actions. Most of our classifiers learn from smartphone sensors, but increasingly our algorithms ingest from wearable sensors such as the Microsoft Band, Apple Watch, and upcoming projects from Google and Samsung. Our approach to building machine-learning-driven applications learns from evidence-based psychosocial intervention practices in mental health, but embodies continuous cardiovascular, skin, and movement-based sensor data in order to arrive at profound but granular insight for the individual, and their care or employer circle.

Researcher and entrepreneur, Davide leads BioBeats’ engineering team as CTO. He is a specialist in the intersection between Artificial Intelligence and music, previously ran a distributed software consultancy company in Italy for ten years. His PhD in Computer Science focuses on models that discover latent variables in performance profiles.